Intelligent performance-based product recommendation system

Abstract

Systems and methods of utilizing communications networks and multivariate analysis to predict or recommend optimal products from a predefined population of commercially available products are disclosed. The recommendations are based on intelligence contained in processing elements and subjective and/or objective product information received from consumers or input to the systems as part of their initial setup. The output of the systems comprise sets of products that they predict the consumer will prefer and/or perform well for the problem or concern identified by the consumer. The performance and preference predictions are a function of consumer problems and product responsiveness patterns. Objective product information is generally obtained with diagnostic instruments. Data measured with the diagnostic instruments may be communicated to the data processing portions of the invention via the Internet. The outputs of the data processing portion of the system may be presented to consumers via the Internet as well.

What is claimed: 
     
         1 . A method for improving product recommendation quality, the method comprising:
 generating a plurality of individualized product recommendations for a given concern with an intelligent performance-based product recommendation engine;   receiving feedback from a plurality of consumers on use of products to treat the concern; and   re-training the product recommendation engine based on the feedback received.   
     
     
         2 . The method of  claim 1 , wherein the receiving feedback step comprises receiving product preference data. 
     
     
         3 . The method of  claim 1 , wherein the receiving feedback step comprises receiving product performance data. 
     
     
         4 . The method of  claim 3 , wherein the receiving product performance data step further comprises receiving target substrate condition data. 
     
     
         5 . The method of  claim 3 , wherein the receiving feedback step further comprises receiving subjective product performance data. 
     
     
         6 . The method of  claim 3 , wherein the receiving feedback step further comprises receiving objective product performance data. 
     
     
         7 . The method of  claim 6 , wherein the receiving objective product performance data step comprises receiving diagnostic data. 
     
     
         8 . The method of  claim 1 , wherein the re-training step comprises adjusting values of a plurality of product attributes in a neural network, the neural network comprising a portion of the product recommendation engine. 
     
     
         9 . The method of  claim 1 , wherein the re-training step comprises adjusting values of a plurality of connection weights in a neural network, the neural network comprising a portion of the product recommendation engine. 
     
     
         10 . The method of  claim 1 , wherein the re-training step comprises identifying relevant consumer segments based on the feedback and grouping the consumers into the segments. 
     
     
         11 . The method of  claim 1 , wherein the re-training step comprises revising a set of collaborative neighborhood configurations in a collaborative filter, the collaborative filter comprising a portion of the product recommendation engine. 
     
     
         12 . The method of  claim 1 , wherein the re-training step comprises adding a personal profile information filter to a set of collaborative neighborhood configurations in a collaborative filter, the collaborative filter comprising a portion of the product recommendation engine.